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Size of breeding population

Size of breeding population. Dag Lindgren and Darius Danusevicius DaDa March 2004. For some reason all slides are not visible from my portable, but from other computers. Start with two other matters.

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Size of breeding population

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  1. Size of breeding population Dag Lindgren and Darius Danusevicius DaDa March 2004 For some reason all slides are not visible from my portable, but from other computers

  2. Start with two other matters • The Swedish long-term breeding is still rather open as most of it has not really started yet, thus easy to reconsider. • A number of possible limitations on breeding cycler has to be mentioned and discussed

  3. Swedish long-term breeding - Intentions 20+ subpopulations with BP=50+ (Gpop and Tpop) for spruce and pine Norway spruce • Karlsson & Rosvall (1993) suggest 40*14 ramets per parent in a 20 year cycle Scots pine • Wilhelmsson & Andersson (1993) made a suggestion, which depends on the success of progeny-testing F1 uncertain cycling time

  4. Swedish spruce breeding status 2002 per subpopulation

  5. 500 F1 Candidates Station pc evaluation F2 20.000 F2 progeny trials 50 P, dpm selection 50 F1, dpm 0 2 12 15 25 Year Standard pine strategy Picture stolen from Bengt Andersson 2001http://www.metla.fi/tapahtumat/2001/nordicgenetics/Bengt_Andersson.ppt

  6. Swedish Scots pine breeding status 2002 per subpopulation

  7. Scots pine - addition • Tpop 11 has run a whole cycle to F2, seems “phenotypic” in DaDa language. • Tpop 17; OP seeds have been collected from 20 year old F1; 8 progeny tests with 25000 plants. If cycle length is 25 and BP=50, 25000/(50*25)=20 +, so if that was typical annual cost for pine would be >20 trees per BP parent and year, seems “phen/prog” in DaDa language • I could find only a single case (Tpop17) of major progeny-testing F1 initiated. Things take time….

  8. That long-term breeding takes much time is no surprise. Thanks to the current good documentation (Annual status rapports are available), we will better grasp the time-line and avoid delays. Long-term breeding has not proceeded far for most Swedish populations, thus the methods of long term breeding are not well-established or based on much experience, but open for discussion. It has been thought that methods to force early flowering to get progeny-test initiated really early on pine should become important. This has not yet been done for a single Tpop. Our calculations indicate that this line of breeding is less efficient than progeny-testing of field-tested F1 genotypes.

  9. BP N=50 Alternative pine strategy (cheap) Top grafting Polycross Top grafts (5*20) Planting Planting Progeny test 20*5*40 PC + 30*5*20 OP = 7000 Next Cycle BP DPM DPM Select 5 best in 20 best fam.s Select 5 in worse 30 fam.s Grafting Graft archive (5*30) pollen Field test 5000 F1 (50*100) 30 0 12 2 18 Year (approx)

  10. Phenotypic vs progeny • In future seed orchards tested clones will probably be preferrable to somewhat better - but untested - fresh 15 year old phenotypic selections • Progeny offers options which phenotype does not (like observations on survival, estimates of genetic correlations and other parameters).

  11. Constraints and limitations of breeding cycler • Shit in – shit out, entries must be chosen with care. Sometimes they are not important but sometimes they are • The input values may need some adjustment from the most evident for considering factors not considered in the math • Breeding heads for an area and gets information from a limited number of sites with limited materials, this can be considered by a reduction of CVAm • The test environments may not be considered a sample of future forest environments, this can also be considered by a reduction of CVAm.

  12. Constraints and limitations of breeding cycler –continued 1 • If ever leading for the details for Sweden (or elsewhere) I recommend breeding cycler to be rerun after a more engaged debate about the inputs. We are willing to do the reruns. • Any decision support tool may need modifications for conciderations beyond the model. • Breeding heads for improvement in many characters, we set the goal as one character “value for forestry” and the observed character can be seen as an index with as high correlation as possible to the goal. • The “observation” is an index of observations, and J*M is thought of as time development of observed vs goal.

  13. Constraints and limitations of breeding cycler – continued2 • Plant cost is seen as independent of age of evaluation. This can cause problems for some type of comparisons (e.g. expensive flowering induction). This difficulty can be overcome by inserting different costs for different compared alternatives. • It is easy to add many types of considerations to the EXCEL sheet, but it makes it difficult and complex for the user and journal papers. Those who want a special feature can often rather easy program it into the existing breeding cycler or even I can do it if you ask. • Phenotypic preselection reduces the genetic variance somewhat in our pheno/progeny calculations, quantitatively the effect is negliable (3%) in our main scenario. • The gain by within family phenotypic selection may be slightly overexegarated with large families. This effect is probably small and very depending on as well the experimental lay-out as the evaluation method.

  14. Constraints and limitations of breeding cycler – continued 3 • The optimal breeding strategy is too chaotic to be found by formulas, stochastic simulation is needed instead of breeding cycler.

  15. Constraints and limitations of breeding cycler – continued 4 • Breeding cycler does not specifically link to seed orchards. To cream off seed orchards with sufficient diversity and little inbreeding is the most important reason for tree breeding! But this is taken care of by the choice of the diversity coeff (penalty).

  16. Constraints and limitations of breeding cycler – continued 5 • Genetic correlation are likely to change over generations • Breeding cycler considers the first cycle, the cumulative effect of some cycles can be assumed to be additive, but after a number of cycles this oversimplifications become unrealistic.

  17. Swedish Norway spruce breeding 140 10 clone clonal mixture 120 100 breeding pop 80 10 clone seed orchard 60 40 20 0 0 1 2 3 4 5 6 7 8 9 10 11 Generation Rosvall, Lindgren & Mullin 1998 Conclusions:Accumulative progress for many generationsOrchard progress follows breeding pop progress

  18. Inbreeding follows group coancestry Simulation of Swedish Norway spruce breeding programby POPSIM, BP=48, DPM, equal representation (2/parent) 0.08 0.06 f Probability of identity by descent 0.04 0.02 Conclusions:Accumulative change over many cycles 0 0 2 4 6 8 10 Generations Rosvall, Lindgren & Mullin 1999

  19. Cycling will accumulate gain. Where is the limit?

  20. Balanced long-term breeding • Some unbalance is favourable • The inoptimality loss seem to be small; it is tricky to utilize unbalance, and the balance is unlikely to affect recommendations much.

  21. Imbalance at initiation of breeding At initiation of breeding there is no “balance”. Truncating tested plus trees to long term breeding has sometimes been done with inoptimal imbalance. I believe it is more optimal to sacrify the gene diversity in the initial selections slower. This has been discussed i förädlingsrådet 1999 (see link on seminar page), and one argument is in Routsalainen’s thesis (2002).

  22. Sweden started imbalanced Swedish recently decided to decrease the breeding population drastically (= very low Ne first generation), 6000 plus trees  1000 founders

  23. Flowering – when? • We assume flowering in phenotypes of conifers at ages around 11. That may work with top-grafting. To make progeny-testing with open pollination possible may take 20 years. • It is important that hard data on flowering time and flowering variations in modern progeny trials are collected and well documented. • In our current figures we probably have discounted early flowering assuming pollen collection, hormone injections and top grafting whenever possible. It is also important to document and systematize how efficient such actions are.

  24. More constraints on breeding population number • An unpublished manus, which was produced during a hectic month in the autumn 2003, is not much fuel • Results are contraintuitive to me, before the study I thought the study would suggest larger population size • Decrease of breeding population is something one should be very conservative about • No immediate reason to go downward!

  25. Earlier considerations on breeding population number • Most considerations (including Swedish) is to choose the lowest number, which ensures sustainability and conservation and assurance against loss of alleles, not optimal trade-off with gain. • It has not been well investigated if the optimal number could be higher than applied.

  26. Genetic progress as function of Ne Expected gain after 1, 5, 10, and infinite () generations of selection for different values of Ne in a model population (Baker and Curnow 1969 from Johnson et al 2001 http://www.fs.fed.us/pnw/pubs/journals/Johnson_StClair_Lipow_2001.pdf). Generation Ne 1 5 10  4 3.3 12.4 19.5 28.4 16 3.3 16.0 31.4 114.5 32 3.3 16.8 34.6 177.5 64 3.3 17.2 36.4 220.9 256 3.3 17.5 37.8 240.0 3.3 17.6 38.0 240.0 Ne > 50 in one subpop and much larger in the metapop, so we may not constrain future genetic progress

  27. BP sizes reported in tree improvement programs from Johnson et al 2001http://www.fs.fed.us/pnw/pubs/journals/Johnson_StClair_Lipow_2001.pdf).

  28. Comment: The Swedish breeding population seems unusually large (this may be justified by the ecological amplitude covered by the Swedish breeding program)

  29. The current Swedish breeding program is sustainable for more than 10 generations.Ten generations downstream will offer new unknown options. There is gene banking and natural resources besides breeders activities. The current breeders responsibility do not stretch longer.Question 1: Can it be made more narrow to save money or to boost gain without loosing sustainability?Question 2: Are there gains to be made by enlarging BP

  30. How many are needed and desired?

  31. Summary Census number 50 per subpopulation and 1000+ in meta-population for pine and spruce is OK to continue in the coming decades.

  32. Optimal breeding population size • What is more beneficial at a fixed budget: larger Breeding population (diversity) and smaller testing pop (test precision) or vice versa? • Find the breeding population size which maximizes the annual progress in group merit under the annual budget

  33. How optimally allocate the resources Test precision (testing pop.) Diversity (breeding pop.)

  34. Main findings • Spruce, BP < 50 is beneficial, as clonal test= higher benefit from the gain-generating capacity, • Pine, BP ~ 50, as the gain-generating capacity of the testing strategy is not powerful enough to motivate reduction of gene diversity.

  35. Main scenario Alternative Parameters values scenario values Testing strateg y Phenotype Clone; Progeny 2 ( s 1 - Additive variance ) A 2 ( s 25 - Dominance variance, % of the additive variance ) D 2 2 s 0.1 0.5 Narrow - sense heritability ( h ) (obtained by changing ) E Additive standard deviation at mature age within family ( CV ), % 10 5 to 20 by 2 Am Weighting factor for diversity loss per cycle ( c ), % 200 50 to 1000 by 50 1 (Phenotype) Time before establishment of the selection test, years 5 (Clone) - 17 (Progeny) Rotation age ( RA ), years 60 - Cost for cycling a BP member ( C ), € 30 - RECOMB 0.1 (Clone) Cost per test genotype ( Cg ), € - 1 (Pr ogeny) Cost per test plant ( Cp ), € 1 - Annual budget for all breeding population, € (the constraint) 500 50 to 2000 by 50 Annual progress in Group Merit ( GM/Y ) To be maximized 1 Parameters and scenarios

  36. 20 0.4 Clone 60 17 Annual Group Merit, % 0.2 Phenotype (main) Progeny 0.0 0 0 100 100 200 200 300 300 400 400 500 500 Breeding population Testing strategy Annual progress would benefit from lower BP for spruce, where testing can be clonal!

  37. Testing strategy • For Phenotype: the optimal is above 50, but only slightly. However, if budget is higher, it is better to increase breeding pop size beyond 50, than to increase offspring size. • For Progeny the low optimum may reflect high testing cost, the total cost must be reduced by low BP size. Note that progeny is superior to phenotype at very high budget! • For a mixedPhenotype/Progeny philosophy, similar low numbers as for progeny appeared. • For Clone, low BP size probably reflects the high gain, which makes the gene diversity loss important

  38. Heritability 0.80 30 h2= 0.5 0.60 Annual Group Merit, % 0.40 60 0.20 h2= 0.1, main scenario 0.00 0 100 200 300 400 500 Breeding Population Size High heritability boosts gain and makes loss of gene diversity less important. Low heritability justifies higher BP size, but the dependence is not strong, and I suggest we can regard the dependence as unimportant.

  39. Annual Budget 100 Optimal BP Size 50 0 0 500 1000 1500 2000 Annual Budget (main scenario 500) Optimal BP size increases with the budget, but only marginally and unimportant. But if spruce has a higher budget than pine, it is also an argument for a larger BP.

  40. Optimum share of resource invested in testing increases with the budget 100 80 60 Testing cost as % of budget 40 20 0 0 500 1000 1500 2000 Annual budget

  41. Cost of gene diversity 200 150 main scenario = 200 Optimal BP Size 100 50 0 0 100 200 300 400 500 600 700 800 900 1000 Diversity cost (inbreeding depression = F = 100) Optimal BP is very dependent (close to linear) on cost of gene diversity. It is critical and is difficult to assign a value. Main scenario approach is that the cost is double as high as if all production were lost if no gene diversity remains. This seems sufficiently conservative.

  42. 200 150 Optimum BP size 100 50 0 0 5 10 15 20 Genetic variation in value for forestry, CV main scenario=10 % Genetic variation Optimal BP size is very dependent on genetic variation in value for forestry. It ought to be possible to assign better estimates as trials grow older. The main scenario approach is that the CV of value for forestry is 10% within family, an educated guess based on estimates from younger trials is 12.5% (which may be adjusted to 10 for uncertanties).

  43. 200 150 Optimum BP size 100 50 0 0 5 10 15 20 Genetic variation in value for forestry, CV main scenario=10 % Genetic variation We are now exploring genetic variation in value for forestry in old trials, if we find that to be smaller than CV=10%, that may be reason to increase breeding population size.

  44. Less balance in spruce!? • Economically more important for Sweden (BP) • More plants produced (BP) • Higher investment in breeding (BP) • Higher site index (?) • More flexible, present populations may later be merged (BP) • Can be more efficiently bred by clonal testing (BP) • A lower BP may be defended. Instead I suggest to breed more aggressively, thus less balance in the selections.

  45. Spruce breeding population • We have decided that 50 is needed and should not be keen on reducing it only a decade later. • However there is another reason to reduce spruce BP, that is that it is more flexible, thus zones can be larger. • I suggest to manage spruce BP more unbalanced than pine. • And be more prepared for a reduction some decades ahead by reducing the number of populations.

  46. For pine it is more important to go on with present BP • Lower investment in breeding ( = BP) • Less flexible, more difficult to draw on adjacent zones ( = BP) • Can not be bred by clonal testing (= BP) • Thus for long term breeding insufficient reason to decrease or increase BP and it may be desirable to keep breeding rather balanced.

  47. End of the slidesShall we have a final discussion? Or someone may have tried Breeding Cycler and experienced a problem?

  48. End slide beer Or just relax?

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